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831
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ShareScore release 0.9.0
Dataset results
831 results for “automaticity”
Data from: Automatic facial mimicry in response to dynamic emotional stimuli in five-month-old infants
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Data from: Automatic recognition of self-acknowledged limitations in clinical research literature
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Fast saccadic eye-movements in humans suggest that numerosity perception is automatic and direct.
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Automatic analysis of cochlear response using electrocochleography signals during cochlear implant surgery
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Data from: Automatic detection of key innovations, rate shifts, and diversity-dependence on phylogenetic trees
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Drones, automatic counting tools and artificial neural networks in wildlife population censusing
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Data from: A novel cascade classifier for automatic microcalcification detection
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Data from: Automatic supporting system for regionalization of ventricular tachycardia exit site in implantable defibrillators
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Our Dreams, Our Selves: Automatic Interpretation of Dream Reports
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Use of a pressure wire for automatically correcting artifacts in phasic pressure tracings from a fluid-filled catheter
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Data from: Repertoire-wide gene structure analyses: a case study comparing automatically predicted and manually annotated gene models
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Fast likelihood calculations for automatic identification of macroevolutionary rate heterogeneity in continuous and discrete traits
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Automatic Labeling of Special Diagnostic Mammography Views from Images and DICOM Headers
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A performance comparison of eight commercially available automatic classifiers for facial affect recognition - raw data
<p>Raw data of the videos processed from the UT-Dallas and BU4-DFE datasets by human observers and automatic classifiers.</p>
An annotated visual dataset for Automatic weed detection and identification
<p>This dataset is provided and linked to the Paper :<br> <strong>Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots, </strong><em>in APPS, Special Issue: Machine Learning in Plant Biology: From Genomics to Field Studies</em></p> <p> </p> <p>The training and test images are provided there.</p> <p><br> This research received financial support from the Agence Nationale de la Recherche (grant No. ANR-17-ROSE-0003).</p>
I Sit But I Don't Know Why: Integrating Controlled And Automatic Motivational Precursors Within A Socioecological Approach To Predict Sedentary Behaviors
<p>This file contains the raw dataset analyzed. </p> <p>Sheet 1 contains scored variables.</p> <p>Sheet 2 presents a description of the variables. </p>
Database for Automatic Risk Adjustment in Short-Term Electricity Markets
<p>Database (2014-2018) used in the paper entitled "Automatic Risk Adjustment in Short-Term Electricity Markets".</p> <p>This database includes :</p> <ul> <li>the inputs and outputs of the deep learning probabilistic forecaster which predicts the Belgian system imbalance;</li> <li>the market data related to the construction of the real-time imbalance settlement mechanism;</li> <li>the inputs and outputs of the Machine Learning-based modules, which allow to continuously adjust the optimal risk policy of an actor to the dynamically changing market operating conditions.</li> </ul> <p>These data are obtained from the Belgian Transmission System Operator (Elia) and the European Network of Transmission system Operators (ENTSO-E).</p> <p>If you use these data, please refer to the following paper:</p> <p>J. Bottieau, K. Bruninx, A. Sanjab, Z. De Grève, F. Vallée and J-F. Toubeau, “Automatic Risk Adjustment in Short-Term Electricity Markets”.</p>
Automatic Requirements Testability Analysis Dataset
<p>This repository contains the data and results from the paper “<a href="https://link.springer.com/article/10.1007/s00521-024-09730-x" target="_blank" rel="noopener">Requirement testability measurement based on requirement smells</a>”. We announce a public dataset of software requirements along with their quality issues to facilitate the development and evaluation of software requirement quality tools. The Automatic Requirement Testability Analyzer (ARTA) is a web application to measure requirement testability based on the requirement smells. It facilitates the requirement engineering quality assurance process and allows requirement engineers to manage the requirements of their projects.</p> <p>ARTA dataset is a collection of requirements along with their smells and degree of testability of each requirement. It has been used in the initial evaluation of ARTA testability measurement algorithms. To the best of our knowledge, there is no public dataset for developing and evaluating the requirements smell detector tools.</p> <p><strong>Dataset Structure</strong></p> <p>ARTA dataset contains 4752 software requirements extracted from 24 project documentation (in English). The ARTA dataset (release 1.0.0) is available as a collection of Microsoft Excel files. The <em>Datasets</em> directory contains five Excel files, described as follows:</p> <ol> <li><strong>DS1.xlsx:</strong> This file contains 985 requirements from 6 projects and nine types of smells manually labeled for each requirement.</li> <li><strong>DS2.xlsx:</strong> This file contains 1092 requirements from 8 projects and nine types of smells manually labeled for each requirement.</li> <li><strong>DS3.xlsx:</strong> This file contains 1522 requirements from 6 projects and nine types of smells manually labeled for each requirement.</li> <li><strong>DS4.xlsx:</strong> This file contains 1153 requirements from 4 projects and nine types of smells manually labeled for each requirement.</li> <li><strong>SmellyWordsDictionary.xlsx:</strong> This file contains 1000 most frequent words in computer science domains, which is sorted based on their similarity. Four types of requirement smells are detected based on the words in the smelly words dictionary. For each word, the type of smell has been determined manually by experts.</li> </ol> <p>The <em>DS1_Evaluation </em>directory contains the result of different smell detection and testability measurement algorithms, described as follows:</p> <ol> <li><strong>001_dataset1kv1.xlsx (primary dataset):</strong> The same DS1 in the Datasets directory. This file contains 985 requirements from 6 projects and nine types of smells manually labeled for each requirement.</li> <li><strong>002_dataset1kv1_smell_frequency_with_testability.xlsx:</strong> This file contains the number of words of each requirement, the number of smelly words, number of smelly words according to the type of smell, degree of cleanness, and testability of each requirement, which has been computed based on “001_dataset1kv1.xlsx”. The requirements are in the same order in “001_dataset1kv1.xlsx”.</li> <li><strong>003_dataset1kv1_ARTA_result.xlsx: </strong>This file contains the result of automatically detected smells by <em>ARTA</em>, our proposed smell detector tool. The requirements are in the same order in “001_dataset1kv1.xlsx”.</li> <li><strong>004_dataset1kv1_ARTA_result_smell_frequency_with_testability.xlsx:</strong> This file contains the number of words of each requirement, the number of smelly words, number of smelly words according to the type of smell, degree of cleanness, and testability of each requirement, which has been computed based on “003_dataset1kv1_ARTA_result.xlsx”. The requirements are in the same order in “001_dataset1kv1.xlsx”.</li> <li><strong>005_dataset1kv1_Smella_result.xlsx:</strong> This file contains the result of automatically detected smells by <em>Smella</em>, another smell detector tool. The requirements are in the same order in “001_dataset1kv1.xlsx”.</li> <li><strong>006_dataset1kv1_Smella_result_smell_frequency_with_testability.xlsx:</strong> This file contains the number of words of each requirement, the number of smelly words, number of smelly words according to the type of smell, degree of cleanness, and testability of each requirement, which has been computed based on “005_dataset1kv1_Smella_result.xlsx”. The requirements are in the same order in “001_dataset1kv1.xlsx”.</li> </ol> <p> </p>
Data from: Diel activity, frequency and visit duration of pollinators in focal plants: in situ automatic camera monitoring and data processing
Data collection on interactions between organisms and their environment has traditionally been conducted by on-site human observations, a time-consuming enterprise that could explain the shortage of around-the-clock observations of free-ranging wild animals. In this paper, I outline a time-efficient procedure to collect data on flower-visiting animals. The objectives were, first, to model diel activity rhythms by using cosine-based mixed-effects regression models (cosinor method) on data from an established automatic video monitoring system and, secondly, to test the use of a cheap off-the-shelf digital camera modified for automated monitoring of flower visitors. Two different model systems were studied: foraging bumblebees visiting focal white clovers, monitored around-the-clock (193 h) to model diel activity; and honeybees visiting thistles, monitored over a shorter period (5 h) to test the applicability and reliability of a new method for monitoring pollinators. The data were automatically entered and processed using R-scripts after manual filtering of the images, obviating the need for manual data entry prior to analysis. For diel activity in bumblebees, the model that gave the best fit included the 24-h fundamental period and one harmonic, a 12-h period to modulate the signal, together with temperature. The bumblebees were exclusive diurnal, with activity starting about 5 h after sunrise, peaking sharply in the afternoon and ending about 1 h before sunset. In addition to time of day, activity also increased with temperature. The off-the-shelf digital camera, Canon PowerShot®, with motion detection script, was triggered by every flower-visiting honeybee. In addition to recorded visitor frequency and visitor duration, it enabled high-resolution images, which could be important for species identification. Automatic camera recording is advantageous for close-up monitoring, compared with continuous video recording, because the latter demands more time and effort in reviewing the material. It could be used to study a range of different species such as pollinators, on-plant behaviour of herbivorous animals, cavity dwellers or cavity breeders. Moreover, the procedures for automatic data entry, data processing and statistical analysis for modelling diel activity rhythms could have great relevance for researchers using other types of camera monitoring systems operating 24 h per day.
Data and R script for publication: The LOKI underwater imaging system and an automatic identification model for the detection of zooplankton taxa in the Arctic Ocean
<p>This is a www.zenodo.org data and R script upload for publication:</p> <p> </p> <p>Schmid, M.S. et al., The LOKI underwater imaging system and an automatic identification model for the detection of zooplankton taxa in the Arctic Ocean. Methods in Oceanography (2016), http://dx.doi.org/10.1016/j.mio.2016.03.003</p> <p> </p> <p>Downloadable script: Script_Schmid_Mio_2016_data_upload.R</p> <p>Downloadable data: Schmid_2016_MIO_CGlac.csv</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.